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INV ITEDP A P E R

Very-High-Resolution AirborneSynthetic Aperture RadarImaging: Signal Processingand Applications

By Andreas Reigber, Senior Member IEEE, Rolf Scheiber, Marc Jager,

Pau Prats-Iraola, Member IEEE, Irena Hajnsek, Senior Member IEEE,

Thomas Jagdhuber, Student Member IEEE,

Konstantinos P. Papathanassiou, Senior Member IEEE, Matteo Nannini,

Esteban Aguilera, Stefan Baumgartner, Student Member IEEE, Ralf Horn,

Anton Nottensteiner, and Alberto Moreira, Fellow IEEE

ABSTRACT | During the last decade, synthetic aperture radar

(SAR) became an indispensable source of information in Earth

observation. This has been possible mainly due to the current

trend toward higher spatial resolution and novel imaging

modes. A major driver for this development has been and still is

the airborne SAR technology, which is usually ahead of the

capabilities of spaceborne sensors by several years. Today’s

airborne sensors are capable of delivering high-quality SAR

data with decimeter resolution and allow the development of

novel approaches in data analysis and information extraction

from SAR. In this paper, a review about the abilities and needs

of today’s very high-resolution airborne SAR sensors is given,

based on and summarizing the longtime experience of the

German Aerospace Center (DLR) with airborne SAR technology

and its applications. A description of the specific requirements

of high-resolution airborne data processing is presented,

followed by an extensive overview of emerging applications

of high-resolution SAR. In many cases, information extraction

from high-resolution airborne SAR imagery has achieved a

mature level, turning SAR technology more and more into an

operational tool. Such abilities, which are today mostly limited

to airborne SAR, might become typical in the next generation of

spaceborne SAR missions.

KEYWORDS | Applications; high resolution; signal processing;

synthetic aperture radar (SAR)

I . INTRODUCTION

Today, we are on the edge of a new era of operational

synthetic aperture radar (SAR) systems. Recent years have

shown a rapid development of advanced SAR technology;

new sensors embody several novel features, such as higher

resolution, partial and full polarimetry, advanced interfer-

ometric setups, and new innovative imaging modes.Experimental and operational airborne sensors have been

a major driver of this development, since they are usually

ahead of the abilities of spaceborne sensors by several years.

Manuscript received February 13, 2012; revised May 24, 2012; accepted

August 25, 2012.

A. Reigber, R. Scheiber, M. Jager, P. Prats-Iraola, T. Jagdhuber,K. P. Papathanassiou, M. Nannini, E. Aguilera, S. Baumgartner, R. Horn,A. Nottensteiner, and A. Moreira are with the Microwaves and

Radar Institute, German Aerospace Center (DLR), Wessling D-82234, Germany

(e-mail: [email protected]).

I. Hajnsek is with the Microwaves and Radar Institute, German Aerospace Center

(DLR), Wessling D-82234, Germany and also with the Swiss Federal Institute of

Technology, Zurich 8092, Switzerland.

Digital Object Identifier: 10.1109/JPROC.2012.2220511

| Proceedings of the IEEE 10018-9219/$31.00 �2012 IEEE

mailto: [email protected]


Particularly, the availability of high-resolution SARdata is currently opening a wide field of new applications.

SAR data, due to their inherent speckle effect, appear

blurred and noisy when compared to optical remote sens-

ing data of the same level of detail. Only on speckle-free,

point-like, or linear targets with strong reflection (typically

man-made structures or vehicles) the real resolution capa-

bilities of SAR are fully developed. Therefore, to achieve a

similar interpretability as optical data, SAR data of signi-ficantly higher resolution are usually needed. Recent SAR

sensor systems are capable of resolutions down to few de-

cimeters, resulting in images of excellent quality compa-

rable to modern submeter optical systems. This, together

with the all-weather day-and-night imaging capabilities, is

turning SAR into an ideal tool, particularly for regular

monitoring and mapping applications, where a high relia-

bility of the remotely sensed data is essential.Radar images contain quite different information than

images obtained from optical or infrared sensors. While in

the optical range mainly molecular resonances on the ob-

ject surfaces are responsible for the characteristic object

reflectivity, in the microwave region, dielectric and geom-

etrical properties become relevant for the backscattering.

Radar images therefore emphasize the relief and morpho-

logical structure of the observed terrain as well as changesin the ground conductivity, for example, caused by differ-

ences in soil moisture. Because of the sensitivity to dielec-

tric properties, SAR images, in principle, also can provide

information about the condition of vegetation, an impor-

tant fact for agricultural and forestry applications. Another

important feature of SAR data results from the propagation

characteristics of microwaves. Microwaves are capable of

penetrating into vegetation and even the ground up to acertain depth [1]. The penetration capabilities depend on

the wavelength as well as on the complex dielectric con-

stants, conductivities, and densities of the observed tar-

gets. Shorter wavelengths, like the X-band (�3 cm), show

typically a high attenuation and are mainly backscattered

on the surface or on the top of the vegetation. Conse-

quently, at these wavelengths, information about this layer

is mainly collected. Longer wavelengths, like L- and P-band(�25 cm and �100 cm, respectively), normally penetrate

deep into vegetation, snow and ice, and often also into the

ground. The backscattering then contains contributions

from the entire volume.

Additional to conventional, single-channel SAR, two

multichannel extensions have gained a lot of attention

during the last years: interferometric SAR (InSAR) and

polarimetric SAR (PolSAR). In InSAR, two or more SARimages, acquired from slightly displaced tracks and thus

under slightly different incidence angles, are combined.

Through an analysis of phase differences, these acqui-

sitions allow for the generation of precise large-scale

digital surface models. First InSAR experiments were con-

ducted already in the 1970s and the 1980s, using Jet

Propulsion Laboratory’s (JPL’s) airborne and spaceborne

sensor [2], [3]. However, it was not until the Shuttle RadarTopography Mission in 2000 [4], which provided medium

resolution digital elevation models for most of the land

masses on Earth, that InSAR became a highly recognized

and operational remote sensing tool. Today, the TanDEM-X

mission, consisting of two high-resolution SAR satellites, is

generating a new global digital elevation model of the Earth’s

surface with unprecedented accuracy [5].

Today, InSAR is a powerful and well-established tech-nique, which is operational on most airborne and space-

borne sensors. Besides topographic mapping, an extended

version of SAR interferometry, called differential interfer-

ometry (DInSAR), can be used for precise mapping of

elevation changes [6]. This technique allows the detection

of surface deformations on a subwavelength scale, usually

in the millimeter range. Due to its extreme precision,

DInSAR has found a multitude of applications, rangingfrom the monitoring of ecological stress-change processes

like sudden coseismic displacements or volcanic bulging

before eruptions, over monitoring of man-made subsi-

dence due to mining activities [7]–[9] up to measurement

of glacier dynamics [9]–[11].

SAR polarimetry (PolSAR) is another major extension

of conventional single-channel SAR imaging. Like all elec-

tromagnetic waves also microwaves have a vectorial na-ture, and a complete description of the scattering problem

in radar science requires a vectorial matrix formulation.

This is the task of radar polarimetry, a technique which

was initiated by the introduction of the theoretical concept

of the Bscattering matrix[ by G. W. Sinclair in 1948 [12].

However, it took until the 1980s and the 1990s that high-

quality polarimetric SAR data became widely available

with the growing number of polarimetric airborne sensorslike German Aerospace center’s (DLR’s) E-SAR [13], the

Canadian CV580 system [14], or NASA/JPL’s AIRSAR [15].

One special characteristic of SAR polarimetry is that it

allows a discrimination of different types of scattering

mechanisms. This becomes possible because the observed

polarimetric signatures depend strongly on the actual

scattering process. In comparison to conventional single-

channel SAR, the inclusion of SAR polarimetry conse-quently leads to a significant improvement in the quality of

classification and segmentation results [16]–[18]. Certain

polarimetric scattering models [19] even provide a direct

physical interpretation of the scattering process, allowing

an estimation of physical ground parameters like soil mois-

ture and surface roughness [20], as well as unsupervised

classification methods with automatic identification of dif-

ferent scatterer characteristics and target types [21], [22].Today’s new generation of very high-resolution air-

borne SAR sensors is pushing the limits of what level of

information can be extracted from SAR imagery even fur-

ther. The increased resolution of modern sensors, both

spatially and radiometrically, allows the generation of

novel information products which has been, to date, not

possible with SAR. In many cases, information extraction

Reigber et al.: Very-High-Resolution Airborne Synthetic Aperture Radar Imaging

2 Proceedings of the IEEE |


from high-resolution airborne SAR imagery has alreadyachieved a mature level, while in the spaceborne case high-

resolution applications are still comparatively rare. How-

ever, it can be expected that the next generation of

spaceborne SAR sensors, to be launched in the coming

decade, will bring the novel SAR concepts into regular

operation. In the remainder of this paper, a review of the

current status of mature information products based on

modern high-resolution airborne SAR will be given, to-gether with a description of the current state of the art in

high-resolution signal processing for airborne SAR, form-

ing the base for such kind of applications.

This paper is organized as follows. First, in Section II, a

brief description of the last generation of civilian high-

resolution airborne sensors is provided. The following

Section III is concerned with the specifics and novel con-

cepts needed for high-quality high-resolution airborne dataprocessing. Section IV gives an extensive overview of

various applications based on the availability of high-

resolution airborne SAR data. Finally, Section V concludes

and presents an outlook on upcoming SAR technology.

II . HIGH-RESOLUTION AIRBORNESENSORS

The development of airborne SAR systems for civilian ap-

plications started in the 1980s with the AIRSAR system [15]

from NASA/JPL and the Canadian CV580 system [14]. Inthe 1990s, most systems became fully polarimetric, with

some of them offering multifrequency operation and reso-

lutions in the meter regime. In the last ten years, several

new systems pushed this development further, achieving

resolutions in the decimeter regime as well as offering

multichannel acquisition capabilities (e.g., SETHI,

RAMSES, PISAR-II, F-SAR, and PAMIR). Table 1 gives

an overview of several airborne SAR systems showing theirbasic operation modes. Some of these systems also allow

single-pass interferometric capability in higher frequency

bands by means of a second antenna mounted on a different

position on the fuselage. Alternatively, the SAR antenna

and radar front–end can be integrated in pods under the

fuselage or under the aircraft wings. Repeat–pass inter-

ferometry is also possible in several systems by means of an

accurate flight navigation system, which delivers real-timeposition information of the aircraft to the pilot (or in some

cases, directly to the automatic pilot system). By this, the

parallel flight tracks can be maintained within a few meters

accuracy, allowing the realization of repeat–pass interfer-

ometry. Except for the OrbiSAR, IFSAR, and GeoSAR,

which are commercially operated, all other airborne sys-

tems are designed for research and environmental moni-

toring purposes.As one example of a modern high-resolution airborne

SAR system, in the following, the F-SAR system will be

described in more detail. This new and advanced airborne

SAR instrument has been operated by DLR since 2009 as

the successor to the former E-SAR system. As the E-SAR,

which had been active between 1983 and 2010, also the

F-SAR is intended as a technology testbed to establish exper-

tise in SAR system design, signal processing, and imageanalysis. The F-SAR instrument is constructed mainly from

commercial off-the-shelf components and subsystems.

Design-critical parts, however, such as the antennas and

others, are developed and built inhouse. As the former E-SAR

system, F-SAR is installed and operated onboard DLR’s

Dornier DO228-212 aircraft as the platform of choice.

F-SAR’s main design feature is the fully polarimetric

operation in five frequency bands, X-, C-, S-, L-, and P-band,with the ability to measure different frequency bands and/or

polarizations simultaneously in four recording channels.

Table 1 Overview of Airborne SAR Sensors and Their Main Characteristics. Typical Wavelengths for the Indicated Frequency Bands Are: W-Band: 3 mm,

Ka-Band: 1 cm, Ku-Band: 1.7 cm, X-Band: 3 cm, C-Band: 5 cm, S-Band: 10 cm, L-Band: 25 cm, P-Band: 100 cm, and VHF: 500 cm. OrbiSAR, IFSAR, and

GeoSAR Are Operated Commercially, Others for Research and Environmental Monitoring Purposes

Reigber et al. : Very-High-Resolution Airborne Synthetic Aperture Radar Imaging

| Proceedings of the IEEE 3


Furthermore, the system design features two single-pass

polarimetric across-track interferometers (XTI) with fixed

baselines of approximately 1.6 m in X- and S-bands. In

X-band, there is an additional along-track interferometerwith an 89-cm fixed baseline. In all five bands, repeat–pass

polarimetric interferometry is a standard mode of operation.

Another focus of the design is radar resolution. A resolution

of better than 25 cm in both slant range and azimuth

direction can be achieved in X-band by using a step-

frequency approach to yield an effective signal bandwidth of

760 MHz. Even at P-band, a resolution of 2 m is possible.

Relevant technical parameters are summarized in Table 2.A view into the aircraft’s cabin with the F-SAR hardware

mounted is shown in Fig. 1.

While the P-band antenna is mounted underneath the

aircraft body, a new antenna hold, carrying the X-, C-, S-,

and L-band antennas, is attached to the right side of the

aircraft. This construction of the antenna hold prevents

relative motion between the mount and aircraft frame-works induced by air turbulence and shocks. One aircraft

window has been replaced by a feed-through plate to elec-

trically connect the antennas to the radar inside the cabin.

The F-SAR features also a real-time onboard processing

unit running on dedicated hardware linked to the data

recording units by optical fiber. The onboard processor

presently supports simultaneous two-channel quick-look

and high-resolution processing, as well as the generation ofground moving target indication (GMTI) products. Addi-

tionally, real-time data products can be transmitted to a

ground station via a fast microwave downlink.

III . VERY-HIGH-RESOLUTION SARSIGNAL PROCESSING

In parallel to the evolution of airborne SAR sensors towardincreased resolution there has been a parallel development

of efficient SAR image formation algorithms and motion

compensation procedures. These developments were trig-

gered by the relatively high amount of data to be processed

with modest computing power and the increasing accuracy

requirements with respect to spatial resolution and radio-

metric and geolocation accuracy. In this section, a brief

review of the mostly used image formation algorithms isgiven, followed by the discussions of recent advancements

with respect to motion compensation and radiometric

calibration, all being presented as part of an overall air-

borne SAR processing concept, which is presented first.

A. Airborne SAR Processor ConceptSpatial resolution as well as radiometric and interfer-

ometric calibration accuracy has a direct impact on theability to measure or infer physical parameters from SAR

data. Therefore, it is essential to integrate the best motion

compensation and airborne SAR focusing algorithms avail-

able into a well-defined and consolidated processing con-

cept to ensure optimum focusing performance (see Fig. 2).

New algorithms for motion compensation, antenna pattern

correction, and radiometric SAR data calibration are part

of it. Due to the increased data rate of multichannel very-high-resolution (VHR) systems (e.g., the new F-SAR sys-

tem records up to 20 GB/min corresponding to a factor 120

compared to the former E-SAR system), considerable

effort is required for the efficient implementation of time-

consuming processing steps.

The high-resolution data processing of advanced polari-

metric and interferometric SAR modes (polarimetry,

single- and repeat–pass interferometry, tomographic pro-cessing) is considered the core part of each modern VHR

SAR processor and must include highly accurate topogra-

phy adaptive motion compensation. Preprocessing is usual-

ly required to roughly compensate for the motion of the

aircraft and correct for the nonideal properties of the

transmitted pulses. The processing of data acquired in

step-frequency mode needs also to be supported in some

Table 2 F-SAR Technical Characteristics in the Different Bands:

Radio Frequency (RF), Bandwidth (Bw), Maximal Pulse Repetition

Frequency (PRF), Transmit Power (PT) and Range/Azimuth Resolution,

Sampling, Channels, and Data Rate

Fig. 1. The F-SAR instrument in the cabin of the Do228 aircraft.

Standard instrumentation racks house the radar’s electronic units.




cases (e.g., VHR X-band data of the F-SAR sensor). For thecore 2-D focusing operation, the standard extended chirp-

scaling (ECS) algorithm is used for F-SAR, but other

accurate kernels may be used as well. Highly accurate

radiometric and geometric calibration can be ensured by

an additional processing step after regular focusing. This

additional calibration is based on the full, complex 3-D

antenna pattern (in elevation/azimuth/frequency) and

computes corrections taking into account the flight trackand sensor attitude as well as any variations within the

synthetic aperture. As a result, it can improve the radio-

metric calibration usually performed prior to azimuth

compression and, by correcting interchannel phase offsets,

also the polarimetric calibration. Georeferencing and re-

sampling to geodetic grids need to be performed for mak-

ing the data suitable for integration into any geographical

information system (GIS). Specific postprocessing algo-rithms (e.g., Pol-InSAR, tomographic SAR processing, soil

moisture retrieval) are conveniently performed on slant

range data, and only the resulting final information pro-

ducts are georeferenced.

B. SAR Image Formation AlgorithmsSAR image formation basically consists of a coherent,

phase-corrected integration of the recorded raw data sam-

ples. The different algorithms deviate from each other in

terms of how accurately and efficiently they implement the

spatially adaptive summation. In principle, one can dis-tinguish time-domain algorithms, which are most accurate

but computationally expensive, and frequency-domain

algorithms, which are more effective and generally pre-

ferred for operational data processing.

1) Time-Domain Algorithms: Time-domain algorithms,

often referred to as direct backprojection (DBP), imple-

ment the phase-corrected integration of the raw dataechoes for each imaged point separately by taking into

account the individual two-way propagation delay. There-

fore, they are most accurate and can easily adapt to non-

linear flight geometries or airborne acquisition scenarios

with large motion errors. Due to their computational

complexity of order N3, where N is the number of azimuth

and range samples assumed equal, their use is restricted to

processing small areas and they serve mainly as referenceprocessors for the development of more efficient algo-

rithms. An efficient implementation of direct backprojec-

tion is the fast-factorized backprojection (FFBP) algorithm

proposed in [37]. It implements the coherent integration

within several steps at different resolution scales, most

of them in polar reference systems, thus being able to

reduce the computational complexity to N2 log N. Ini-

tially, it has been proposed as an efficient processor forhigh-resolution, long-wavelength (VHF) SAR focusing,

avoiding the deficiencies of frequency-domain algorithms.

In the context of VHR airborne SAR focusing, it serves as a

valuable reference also for other frequency bands. Adapta-

tions of the FFBP were made to efficiently focus also bi-

static SAR data acquired in combined airborne and

spaceborne geometries [38].

2) Frequency-Domain Algorithms: Due to their efficiency,

the preferred image formation algorithms work in the fre-

quency domain. A helpful comparison of the different

algorithms can be found in [39]. Common to all algorithms

is the assumption of a straight rectilinear flight path,

without taking into account the motion errors of the air-

craft. Among the first algorithms, which were used for

airborne SAR processing, was the range Doppler algorithm(RD), which essentially decouples range and azimuth pro-

cessing at the cost of focusing accuracy. The spatially

Fig. 2. Required steps for VHR SAR data processing for advanced SAR applications, as implemented for the F-SAR system of DLR.




variant range migration correction is performed with in-terpolations. The most exact algorithm is the wavenumber-

domain algorithm, which is known to provide the most

accurate focusing performance by implementation of the

Stolt mapping in the 2-D frequency domain [40]. Consid-

erable efforts have been made toward the development of

algorithms, which avoid the use of extensive interpola-

tions, and the most prominent example is the chirp-scaling

algorithm [41]. It has been adapted as the ECS to theairborne scenario by including a two-step motion error

correction [42], [43] as well as radiometric corrections of

antenna pattern and two-way attenuation, properly taking

into account variations of the roll angle. Also for the

wavenumber algorithm, adaptations have been proposed

for the airborne case, involving a modified Stolt mapping

and including motion compensation [44]. However, for

efficiency reasons, in most cases, the RD or ECS algo-rithms are used for the operational processing, but it needs

to be stated that the algorithm’s focusing accuracy reaches

its limits at these VHRs.

C. Key Motion Compensation AspectsAirborne SAR sensors have been and are still used ex-

tensively for the development and demonstration of new

imaging techniques, often involving several repeated passesover the same area of interest, each being separated by

certain spatial baselines. For Pol-InSAR and SAR tomog-

raphy, predefined spatial baselines, typically separated by

some tens of meters, are flown, whereas for change de-

tection and differential SAR applications, the same nominal

flight track is preferred. In each track, deviations from a

straight flight path occur, which need to be compensated

during processing. The RD and ECS algorithms typicallyimplement a two-step motion compensation: a first-order,

range-invariant correction at raw data level is performed

prior to any presumming or azimuth resampling operation,

while the second step is the range adaptive motion com-

pensation, which is conveniently applied after range cell

migration correction. Each compensation step needs to cor-

rect phase and envelope of the signal according to the line-of-

sight difference between the real and nominal tracks.In high-resolution applications, two problems remain.

First, the accuracy of modern navigation systems is typi-

cally limited to a few centimeters, leaving a certain amount

of uncompensated motion in the data. Second, the accu-

racy of motion compensation in frequency-domain algo-

rithms is limited in itself because of the narrow azimuth

beam assumption, and residual errors might remain in the

data, particularly in case of large deviations from the flighttrack as well as in case of significant topographic variations

in the scene. As a result, there are certain challenges im-

posed by the motion of the aircraft, which has to be con-

sidered independent for each overflight and which leads to

spatial varying interferometric baselines (see Figs. 3 and 4)

and residual motion errors. Any inaccuracy in the mea-

surement and compensation of the platform motion leads

to undesired imaging effectsVphase errors, defocusing,

mislocationVdeteriorating especially the interferometric

phase and coherence.

During the last decade, several advanced algorithms

for improved high-resolution motion compensation havebeen developed. Such activities were mainly driven by

the needs imposed by the Pol-InSAR applications (see

Section IV-D), tomographic imaging modes (see

Section IV-E), as well as by differential airborne SAR

interferometry (see Section IV-C).

1) Topography-Dependent Motion Compensation: During

the development and refinement of the airborne repeat–pass interferometric processing strategy, it was recognized

Fig. 3. Acquisition of airborne interferometric SAR data. Single-pass

acquisition is possible with two antennas mounted on the same

platform and is used for generating digital elevation models (DEMs).

Here, the DEM of the Oberpfaffenhofen area is shown over an area of

3 km � 10 km, data being acquired by the E-SAR system of DLR. The

repeat–pass baselines, variations being compensated during motion

compensation, are used for flexible acquisition of data for differential

interferometric, Pol-InSAR, or SAR tomography applications.

Fig. 4. Real interferometric baselines achieved during INDREX

campaign [45]. Deviations are less than 1 m [root mean square (rms)]

for each of the three passes with a nominal horizontal baseline of 5 m.




that the usual approximations for motion compensationare not sufficient and that topography and residual motion

errors need to be taken into account very precisely [46].

Concerning topography accommodation, this led to the

parallel development of two algorithms, namely, the so-

called precise topography and aperture-dependent motion

compensation (PTA) approach [47], and the subaperture

topography- and aperture-dependent (SATA) algorithm

[48]. A comparison of these algorithms can be found in[49]. They are based on short-time fast Fourier transform

(FFT) codes and thus make an effective use of the quasi-

linear time–frequency correspondence of the SAR azimuth

signal. Its application is imperative, not only for processing

airborne data in differential SAR interferometric mode,

but also for repeat–pass SAR applications in hilly and

mountainous areas in general.

The problem at hand is illustrated in Fig. 5, whichshows that a different motion compensation is required if

the imaged point is not located at the reference height

assumed during the traditional two-step motion compen-

sation. Different range/azimuth-dependent phase errors,

described by

�err;topo;iðr; xÞ ¼ 4�

��rtopo;iðr; xÞ ��rref;iðr; xÞ� �

(1)

are encountered for the different interferometric acquisi-tions i, with � denoting the radar wavelength, r the range

distance, x the azimuth position, and �rref=topo the path

length difference between the real and ideal (reference)

tracks, calculated for a reference height or considering

topography, respectively.

In general, the error amounts to several phase cycles,

as exemplified in Fig. 5. For L-band at a flight altitude of

3000 m and a deviation from the ideal track of 5 m, thephase error amounts to approximately 2� for every 100-m

height difference from the reference level. Note that the

topographic motion compensation is also azimuth aperture

dependent and, therefore, it also compensates variable

azimuth shifts and improves the focusing. Therefore, it

ideally augments all 2-D frequency-domain SAR pro-

cessing algorithms leading to higher quality data even if

interferometry is not required. For the time-domain ap-proaches discussed in Section III-B, topography is consid-

ered inherently.

2) Estimation and Correction of Residual Motion Errors:The performance of standard airborne SAR processing is

limited by the accuracy of the navigation data available to

perform the motion compensation [state of the art is a

combination of inertial unit and Global Positioning System(GPS) sensor]. Although the relative accuracy is very good,

enabling well-focused data, the absolute performance is

limited by the absolute precision of the differential GPS

signal, which is in the order of 5–10 cm (up to one inter-

ferometric phase cycle assuming SAR data at L-band and

the two-way propagation delay). It is obvious that this ac-

curacy is insufficient for repeat–pass interferometry. A

robust error estimation approach has been developedbased on the so-called multisquint technique [50], [51]. It

can be understood as the measurement of azimuth misre-

gistration values, which are then inverted to yield deriva-

tives of the horizontal and the vertical baseline offsets,

which are finally integrated to serve as inputs for com-

puting an azimuth varying phase correction (see Fig. 6).

For a single range position, the approach can be summar-

ized as

�err;resðr; xÞ ¼ 4�

�r

Z�xðr; xÞ dx (2)

where �x stands for the range/azimuth-dependent mis-

registration in azimuth.In the multisquint technique, phase differences of in-

terferograms obtained from multiple spectral looks are

computed, averaged, and then inverted to give robust

estimates of residual baseline errors while considering

coherence weights. Generally, it can be stated that the

technique has some similarity to the assessment of variable

azimuth movement, e.g., of glaciers in spaceborne SAR

Fig. 5. Range difference for motion compensation using a reference

level (approximated, default during 2-D focusing) and assuming the

topography (exact, when using topography adaptive approaches).

Example for topography-dependent phase error for a mountainous

area. Note the coupling between topography and motion errors along

azimuth.




interferometry using spectral diversity [52], [53]. The

technique is also referred to as multiaperture interferom-

etry (MAI) [54]. An example of successful compensation isshown in the lower part of Fig. 6. However, note that

similar to the topography compensation, it is not sufficient

to simply add the computed phase correction to the slave

data. Moreover, it is essential to decompress, compensate,

and recompress the data in azimuth in order to correct also

the second-order effects (wrong localization and potential

azimuth defocusing due to the range misregistration

variation in the original raw data).The accommodation of this technique within the inter-

ferometric SAR processing chain including topography

adaptive motion compensation is shown in the block diag-

ram in Fig. 7. It is important to remark that topography

adaptive motion compensation is required to be performed

prior to the residual baseline error estimation in order to

avoid the misinterpretation of the estimates. Further, the

nominal coregistration only considers the known acquisi-tion geometry, possibly refined by a constant offset esti-

mated from the data.

With multisquint, the differential residual motion

errors (in horizontal and vertical directions) between the

two tracks are inverted from the estimated residual phase

[see (2)]. The estimation is repeated by iteratively process-

ing the slave image. This process avoids underestimating

the residual errors due to the limited coherence of thescene and gives confidence to the retrieved estimates [51].

An example of residual errors estimated for C-band data

with millimeter accuracy is shown in Fig. 8. Without this

compensation approach, phase errors and coherence deg-

radation would occur, impacting seriously the interfero-

metric data quality. In case of azimuth motion (e.g., glacier

flow), an extended multisquint approach has been devel-oped, which decouples residual motion errors from the

real movement in the scene (see Section IV-C and [55]).

As an alternative to the multisquint techniques, ad-

vanced autofocus algorithms may also be considered for

Fig. 6. Linear (residual) motion errors lead to azimuth varying

misregistration in interferometric SAR (top). The applied phase

corresponds to the integration of the estimated azimuth localization

error [see (2)]. Early example for repeat–pass case in L-band (bottom).

Fig. 7. Interferometric processing of airborne data including

2-D SAR focusing algorithm with integrated topography adaptive

motion compensation and multisquint processing for residual motion

error estimation.

Fig. 8. Residual motion errors between master and slave images

estimated by the iterative multisquint approach for a C-band

interferometric pair of E-SAR data. Black: Original residual errors;

green: After one iteration; red: After three iterations. Top: Horizontal

error; bottom: Vertical error.




estimating and compensating independently the residualmotion errors of master and slave images. For example, the

weighted phase curvature autofocus (WPCA) algorithm

was shown to be suitable for multipass airborne SAR in-

terferometry at L-band [56].

D. Accounting for Sensor HardwareThe signal processing pipeline outlined in Fig. 2 makes

the basic assumption that SAR raw data can be compressedin range with a known reference function, i.e., the pulse

transmitted by the sensor. The obvious requirement that

this reference function be characterized with high preci-

sion introduces another aspect to SAR signal processing:

high-quality image formation requires precise knowledge

of the behavior of the numerous hardware components in

the transmit and receive signal pathways.

In practice, modern sensors include a special replicasignal pathway [57], [58], in which the output of the

transmit signal pathway is fed directly into the receive

pathway. The signal thereby recorded then characterizes

the combined impulse response of all sensor components

and, if measurements are taken regularly during data

acquisition, includes transient phenomena, such as tem-

perature variations, which are difficult to model or

measure otherwise. Importantly, these internal measure-ments cannot account for characteristics of the transmit

and receive antennas. The response of an antenna varies

significantly as a function of propagation direction in 3-D

space. Consequently, an appropriate treatment of the

antenna in processing is both important and, in practice,

challenging.

At the outset, the antennas in a SAR system must be

characterized precisely in the form of a 3-D model overtwo angles (elevation and squint) and frequency. In

practice, such models are obtained either from simulations

[59], direct far-field measurements [60], or, with limited

accuracy, from the analysis of acquired SAR data.

The most well-known feature of an antenna is its

overall gain, i.e., the amplification of signals in transit, as a

function of propagation direction. The overall gain over

elevation plays a central role in the radiometric calibrationof data and will be dealt with separately in Section III-E.

Traditionally, other effects have been neglected. As the

bandwidth of SAR sensors increases, however, this as-

sumption no longer holds as it becomes prohibitively dif-

ficult to design antennas that are sufficiently well behaved

over an increasingly large frequency range. An example of

an effect that may need to be considered is a gain that

varies as a function of frequency and, potentially, pro-pagation direction. Unless compensated for, this mis-

estimation of the sensor impulse response leads directly to

a loss of resolution. Another example, which has not in fact

been observed in F-SAR data, would be a signal delay that

varies as a function of propagation direction and would

impair the geometric accuracy and, potentially, the

resolution.

The former example is illustrated in Fig. 9, where the

antenna gain clearly varies over the sensor bandwidth. As

it happens, the associated antenna impulse response is also

variable with respect to the elevation angle, as is evident inthe slight resolution trend of Fig. 10. Since the antenna

impulse response is therefore a function of time (elevation

angle as a function of range time) and frequency, the cor-

rection must be carried out using time-frequency tech-

niques such as the short-time Fourier transform [61].

The results of the correction, which was carried out as a

Fig. 9. Clockwise from the bottom left: The antenna gain Að�;�Þ of the

F-SAR S-band H-polarized antenna at three frequencies within the

processed bandwidth (300 MHz). Bottom right: The antenna phase at

the center frequency f0 ¼ 3.25 GHz. All plots illustrate variations as a

function of the squint and elevation angles and contain information

that is crucial for accurate sensor calibration.

Fig. 10. The range resolution of F-SAR S-band data, in HH polarization,

as a function of the off-nadir angle. The resolution is measured on

trihedral corner reflectors and can be seen to improve once the

impulse response function of the antenna has been taken into account.




postprocessing step, show that the nominal sensor resolu-tion can be restored (see Fig. 10).

E. Radiometry and Phase CalibrationThe calibration of a SAR sensor ensures that all data

products produced by the processor are associated with

well-defined, physical units and can therefore be mean-

ingfully compared and interpreted as absolute values. The

intensity of a SAR image, for instance, usually denotes the

radar backscattering coefficient of the imaged targets and

can be linked to quantitative estimates of soil moisture[62]. The phase of a complex SAR image, meanwhile, is

essential as a measure of sensor-to-target distance in

InSAR applications (as, for example, in Section IV-C) and

plays an equally fundamental role in the derivation of

polarimetric signatures in PolSAR applications.

Sensor calibration is generally a two-step process [63],

the first of which is internal and derives necessary adjust-

ments to image magnitude, phase, and range distance atwhich targets are imaged from known properties of the

sensor (i.e., the sensor impulse response function dis-

cussed in Section III-D) and the imaging geometry. In the

external calibration that follows, these adjustments are

validated based on reference targets with known proper-

ties, such as dihedral or trihedral reflectors or transpon-

ders, and additional corrections are derived. The aim of the

internal calibration is to ensure that these corrections applyeven after the internal configuration of the sensor or the

imaging geometry have changed, such that calibrated data

can be produced without having to rely on reference

targets in the imaged scene.

The internal calibration of SAR image intensity is based

on the radar equation for SAR, which relates the radar

cross section � to the power received by the sensor PR and

is usually written [64]

PR ¼ �PTGsysGproc�

3Að�Þfrð4�Þ2r32�azv

(3)

where PT is the power transmitted, Gsys is the accumulated

gain of all components in the transmit and receive signal

paths, Gproc is the gain due to the SAR processor (it in-

cludes, for instance, the effect of the windowing functions

used for sidelobe suppression), � is the system wave-

length, Að�Þ denotes the two-way antenna gain as a func-

tion of the elevation angle � of a given target, fr is the pulse

repetition frequency, r denotes the range distance to thetarget, �az is the azimuth resolution and v is the platform

velocity.

For VHR airborne SAR data, the computation of � by

rearranging (3) is problematic, since it does not explicitly

account for deviations from the nominal track or the real

sensor attitude angles during the acquisition. In addition,

it does not account for the fact that r and � are not, in fact,

constant but vary within the synthetic aperture. Takingthese effects into account becomes especially important as

the resolution increases and longer integration times ne-

cessarily lead to larger variations within the aperture.

An extended version of (3) that takes these effects into

account can be written [61]

PR ¼ �PTGsys�

2

ð4�Þ2Xt1

t¼t0

GprocðtÞA �ðtÞ; �ðtÞð ÞrðtÞ4

(4)

where t0 and t1 denote the start and stop, in samples, of theintegration time for a given target, Gproc now varies with

integration time due to the azimuth sidelobe suppression

window, Að. . .Þ denotes the 2-D two-way antenna gain as a

function of elevation � and squint �, and the range dis-

tance r changes as the sensor moves toward or away from

the target. The maps �ðtÞ and �ðtÞ take into account the

sensor attitude angles (heading, pitch, and roll) to map a

given target into the 2-D antenna coordinate system. Fi-nally, the radar cross section can be recovered by carrying

out the summation over samples t numerically and rear-

ranging for �. Fig. 11 shows that this approach can lead to

considerably more stable radiometric calibration results

than the simpler alternative in (3).

Assuming that a complex, 3-D antenna pattern

að�; �; fÞ over elevation, squint, and frequency is available,

it becomes possible to precisely remove residual phasevariations from the data. These variations arise when the

phase center of an antenna is not a single point in 3-D

space, but moves as a function of the direction of pro-

pagation (see the phase variations in Fig. 9). This phase

Fig. 11. A comparison of calibration algorithms for X-band F-SAR data

using trihedral corner reflectors in three independent acquisitions.

The RCS error shown denotes the difference between the theoretical

and measured radar cross sections. Red: Standard calibration;

black: Refined calibration with 3-D antenna model.




contribution can be estimated and consequently removed

by evaluating an integral very similar to that of (4)

�ant ¼ argXt1

t¼t0

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiGprocðtÞ

pa �ðtÞ; �ðtÞ; f0ð Þ

rðtÞ2 a �ðtÞ; �ðtÞ; f0ð Þj j

" #: (5)

This equation expresses the fact that the phase in the

focused SAR image is given by the weighted summation of

complex amplitudes with phase information originating at

the center frequency f0 of the sensor. Table 3 suggests that

this type of phase correction can improve the phase stabi-lity of the focused SAR images in the context of PolSAR

analyzes. Similar improvements are to be expected in

InSAR applications.

IV. APPLICATIONS OFHIGH-RESOLUTION SAR

A. VHR Polarimetric ImagingOne of the main drawbacks of SAR data, when com-

pared to optical data, is the inherent presence of speckle

noise in the images. In medium resolution images, natural

surfaces and other distributed targets appear blurred and

noisy to the untrained observer, who has typically the

feeling to analyze an image of much lower resolution or

quality than it actually has. Only on pure point-like orlinear targets with strong reflection (typically man-made

structures or vehicles) the real resolution capabilities of

SAR are fully developed. However, once the sensor’s spa-

tial resolution, and associated, the speckle’s granularity

scale, reaches values significantly below the size of objects

to be analyzed, the effect of speckle backs out and even

fine details, like, for example, textures on distributed tar-

gets, can be examined.Other typical problems when interpreting convention-

al SAR images occur in the case of, for example, urban

areas. The footprint of buildings is generally not correctly

imaged, since a SAR works in a side-looking geometry,

causing strong layover and shadowing effects. Usually,

only an L-shape of the building parts facing the radar is

recovered. Additionally, parts of the roof areas appear

often very dark due to their inappropriate orientation to-ward the sensor. Both effects together render the inter-

pretation of urban areas and buildings in general to be a

complicated task, which usually requires several images

from different geometries.

Recent airborne SAR sensor systems have advanced

into the decimeter resolution range, which apparently is

high enough to cover many requirements of Earth observa-

tion applications, even when considering a certain deficitin image interpretability due to speckle. Additionally, the

availability of polarimetric information makes it possible to

generate false color images and especially to image also

urban structures in a much more convenient way, since

polarization diversity helps circumventing orientation

effects. Such an imagery delivers an almost photographic

impression of the scene under investigation, and inter-

pretation becomes comparatively easy even for untrainedusers.

Fig. 12 shows a fully polarimetric X-band F-SAR image

with 25-cm resolution of a small farm with surrounding

agricultural surfaces. The buildings in the middle of the

scene are well imaged, particularly the roof areas show a

strong response in the cross-polar channel due to their

orientation to the sensor. The different fields appear in

various colors, depending on their respective crop types.Some of the surfaces show significant growth effects (e.g.,

the dark spot in the top left of the image), being an indi-

cation of variations in moisture and/or soil quality. Very

important is also the observation that at this resolution

surface textures become apparent in SAR images. This is

most obvious in the yellow/ocher field in the top middle of

the image: here machining has generated a linear texture

Table 3 The Effect of Phase Corrections Derived From the Complex

Antenna Characterization for Data Acquired by the F-SAR Sensor in

X- and S-Bands. The Standard Deviations Represent the Variation of the

Polarimetric HH-to-VV Phase Difference Measured on Trihedral Corner

Reflectors in a Number of Independent Acquisitions. The Phase Accuracy

Is Seen to Improve by About 20% in Each Case. High Phase Accuracy Is

Required for Polarimetric and Interferometric Applications

Fig. 12. Fully polarimetric F-SAR X-band image with 25-cm spatial

resolution (blue: VV polarization, red: HH polarization, green: HV

polarization). Building shapes and surface textures are clearly visible.




across the field; even some small haystacks are visible. Also

the blue field in the middle right shows a significant linear

texture.

Another example of a fully polarimetric high-resolutionimage can be found in Fig. 13. This image has been

acquired at C-band, a wavelength often considered to be

ideal for agricultural applications. At a spatial resolution of

50 cm, various surface textures can be observed. Partic-

ularly, the yellow field in the middle left shows wind blow

effects overlaid to a light horizontal linear texture corre-

sponding to the orientation of the planting. Also several of

the other fields show interesting textures. Up to now,surface textures in SAR imagery have widely been ignored.

However, they provide an additional and valuable source

of information, allowing, for example, to distinguish or

recognize certain surface types with an otherwise similar

signature.

S-band is a very attractive wavelength, since it already

shows significant penetration capabilities into vegetation

and strong polarimetric effects. At the same time, still veryhigh spatial resolutions can be achieved at S-band. Fig. 14

shows a high-resolution S-band image acquired by F-SAR.

Particularly, the forest is imaged with great detail. If ex-

amined carefully, one can observe some individual trees

along the railroad line in the middle of the scene, which

appear semitransparent due to the penetration capabilities

of longer microwaves at S-band.

At L-band, spatial resolution is lower, due to the longwavelength and the small available bandwidth according to

frequency regulations. However, polarimetry is know to be

strong at L-band,and even dense forest is penetrated up to

the ground. Fig. 15 shows a high-resolution L-band image

of a partly forested area, with some rotating windmills in

the open areas. The coarse texture of the forest canopy is

visible, while the rotating wings of the windmills appear

defocused due to the long integration time.

B. Change DetectionA broad class of SAR applications is based on the ability

to detect temporal changes in an area of interest that is

repeatedly, perhaps even routinely, imaged by SAR sen-

sors. Examples include the monitoring of land use to detect

Fig. 13. Surface textures and wind blow effects in an agricultural

area. Image acquired by F-SAR in C-band with 50-cm resolution

(blue: VV polarization, red: HH polarization, green: HV polarization). Fig. 14. Agricultural area and an alley of trees, appearing

semitransparent at S-band. Image acquired by F-SAR in S-band

with 65-cm resolution (blue: VV polarization, red: HH polarization,

green: HV polarization).

Fig. 15. Rotating windmills close to some forested areas.

Image acquired by F-SAR in L-band with 1.5 � 0.5-m2 resolution

(blue: VV polarization, red: HH polarization, green: HV polarization).




seasonal or other changes, rapid damage assessment in thewake of natural disasters, and surveillance.

High-precision interferometric SAR processing, as de-

scribed in Section III, provides fully automatic means for

generating SAR image pairs or image stacks that are core-

gistered with centimeter precision. In addition to tech-

niques for interferometric analysis, these data can be

compared and exploited to detect changes such as object

movement or local changes in the observed dielectric pro-perties. The availability of VHR SAR data means that even

small-scale changes, such as moving vehicles or damage to

infrastructure, can be identified.

Approaches to change detection generally fall into

three categories: straightforward approaches on the basis

of single-channel intensities, coherent interferometric

change detection, and incoherent change detection on

the basis of polarimetric signatures.In contrast to the straightforward comparison of image

intensities, coherent change detection relies crucially on

the so-called interferometric coherence. This quantity � is

defined as the normalized cross correlation of the complex

master and slave images. Decorrelation between acquisi-

tions, associated with a low coherence, indicates that

change has taken place. Significantly, the coherent nature

of the approach allows for the detection of changes thathave only negligible impacts on the backscattered in-

tensity: as the observed pixel amplitude and phase are

sensitive to the spatial arrangement of all scattering con-

tributions within a resolution cell, coherent change detec-

tion can potentially detect extremely subtle scene changes.

This, of course, also implies a high sensitivity to environ-

mental effects such as wind and rain, which may be irre-

levant to the application at hand.Such applications can benefit from incoherent polar-

imetric change detectors. These are based on the com-

parison of the polarimetric signatures observed at different

acquisition times. The polarimetric properties of a target

are known to be highly sensitive to its size, shape, geom-

etrical structure, and dielectric properties. At the same

time, polarimetric signatures are not sensitive to the spa-

tial arrangement of scattering mechanisms within a reso-lution cell, such that detection results are not affected by

interferometric decorrelation.

The polarimetric change detector derived in [65] has

proven useful in a number of applications. It defines an

indicator of change

� ¼ jC1jNjC2jN

ðC1þC2Þ2

�� 2N (6)

where C1 and C2 denote the N-look sample covariance

matrices in images 1 and 2, respectively, and j � j denotes

the matrix determinant. The detector has the advantage of

being very robust and, if used in conjunction with the

associated test statistic, has a constant false alarm rate with

respect to the statistics of polarimetric speckle noise.An example of high-resolution intensity-based change

detection is shown in Fig. 16. Here, a parking lot of a

logistics company was imaged twice within about one hour

with the F-SAR airborne instrument in high-resolution

X-band mode. A number of cars and trucks have evidently

appeared (blue) or disappeared (yellow) in the time be-

tween data acquisitions. Fig. 17 shows a comparison of

coherent interferometric and incoherent polarimetricchange detection. The coherent detector, shown on the

left, is very sensitive to even the smallest changes such as

movements in wind blown vegetated areas. In contrast, the

polarimetric detector robustly identifies changes in polari-

metric signatures, which can, in this example, be attri-

buted mainly to moving vehicles.

C. DInSAR ApplicationsDifferential SAR interferometry (DInSAR) has become

a powerful tool to measure deformation phenomena at a

large scale [66], [67]. Very high accuracy, in the order of a

fraction of the wavelength, can be attained by exploitingthe coherent nature of SAR systems. Differential SAR

Fig. 16. Monitoring the parking lot of a logistics company using intensity-based change detection. Two images with a multilook resolution of

0.5� 0.5 m2 were acquired with 2-h separation. Changes appear in blue and yellow, and white represents vehicles that have not moved between

both acquisitions.




interferometry using a spaceborne platform is already anestablished technique. The ideal stable trajectory of a sa-

tellite ensures that the SAR processor will be able to pro-

perly focus the data without introducing undesired

artifacts. Also, the fact that large stacks of images are

available for most areas of interest has been of great help to

develop several advanced DInSAR (ADInSAR) techniques

[10], [68]–[70]. The airborne data processing represents a

further challenge, since it is subject to the limitationsimposed by motion compensation, as already mentioned in

Section III-C. However, the advantages an airborne plat-

form can offer are quite appealing: flexibility in sense of

spatial resolution, used wavelength, and data acquisition

(i.e., short revisit time). For example, the radar frequency

plays an important role regarding temporal coherence and

ground penetration, since lower frequency bands (L, P)

tend to have a better long-term coherence and higherground penetration than higher frequency bands. Also, the

flexibility of the data acquisition is a major asset, since the

aircraft can be used to acquire the images with the desired

time intervals and geometries, not having to wait, as it

happens in the orbital case, for the satellite to illuminate

the same area with a similar look angle.

Several papers have been published in the past con-

cerning airborne DInSAR. In 2003, the first airborne dif-ferential SAR interferogram of a large area was presented

in [71], using a classical three-image DInSAR approach

[72]. The authors measured some displacements in agricul-

tural fields that might be related to the penetration depth

of electromagnetic waves due to a different soil moisture,

and also obtained indication of water level change in a

swamp area. In 2004, some controlled experiments withthree corner reflectors measured at both L- and C-bands

were presented in [73], obtaining absolute measurements

with an accuracy better than 1 cm. Also in 2004, the

deformation of a dike measured at C-band was reported in

[74]. In 2008, a time series at X-band was presented in

[75], where the motion of a corner reflector was retrieved,

while in the same year, a time series at L-band was

analyzed in [76], where several differential effects could beobserved over agricultural fields.

As a further example of airborne differential SAR in-

terferometry, Fig. 18 shows the estimated ground defor-

mation in a mining area in Germany. The data were

acquired in 2009 by the E-SAR system at L-band with a

separation of six months. Despite the large temporal

Fig. 17. Change detection in two high-resolution F-SAR X-band scenes acquired with about two days time difference; bright patches indicate

high probability of change. Left: Coherent detection based on interferometric coherence (blue: VV polarization, red: HH polarization,

green: HV polarization). After two days, mainly phase decorrelation within different vegetation types dominates. Right: Incoherent polarimetric

change detection, indicating primarily the vehicles that moved between data acquisitions.

Fig. 18. Estimated ground deformation with overlayed reflectivity

over a mining area in Germany. Data acquired by the E-SAR system at

L-band with six-month separation.




separation, the coherence is still good, allowing the re-trieval of valuable deformation information, even over

vegetated areas. Note also that the high resolution of the

system, about 2� 0.5 m2 (range� azimuth) in the current

case, allows for a larger averaging window, hence achiev-

ing a better phase noise reduction but still delivering a

product with a good geometric resolution. Note that the

subsidence reaches up to 20 cm in some areas.

Another example in terms of data acquisition flexibilityis given in the monitoring of alpine glaciers. Due to the

data scarcity, as well as the lack of field measurements in

such high-altitude and remote scenarios, remotely sensed

SAR data become an attractive alternative. Spaceborne

SAR systems have already proven the possibility to mea-

sure the surface velocity field (SVF) of glaciers [77]–[82],

but the lack of flexibility in the acquisition configuration as

well as the fixed, and usually too large, revisit time, is animportant limitation in many cases. Airborne SAR systems

are, however, not limited by this factors. Furthermore, by

using short revisit times, one can use coherent interfer-

ometric techniques to measure the SVF, which, besides

being more accurate when there is coherence, do not rely

on texture, hence being feasible also in areas with homo-

geneous backscattering.

Fig. 19 shows the estimated 2-D SVF over the Aletschglacier using the E-SAR sensor. The campaign was flown in

2006 at X-, L-, and P-bands in two consecutive days [55].

The motion in line-of-sight (LOS) was estimated using

DInSAR. To measure the along-track displacement (mu-

tual shift), the so-called spectral diversity technique [52]

was used, which achieves the Cramer–Rao bound in the

estimation of the coregistration error [83]. Under the sur-face parallel flow assumption [81], the 3-D SVF can be

retrieved by projecting the 2-D one using the terrain slope,

which in the present case was computed with the X-band

DEM obtained in the single-pass mode. Furthermore, the

E-SAR system was also flown orthogonally to the first pass,

so that different projections of the SVF could be measured

in order to retrieve the 3-D SVF without making any as-

sumption. Fig. 20 shows the final 3-D SVF after combiningthe four measurements of two orthogonal acquisitions.

The monitoring of glaciers is essential to predict their

evolution under the threat of climate change, and the high-

resolution SVF derived with airborne SAR can be used

within ice flow models to improve the estimation of the ice

thickness of the glacier [84].

D. Soil Moisture EstimationSoil moisture is a key parameter in hydrological

modeling that affects a variety of hydrological processes

and is recognized as an emerging essential climate variable

Fig. 19. Retrieval of the 2-D surface velocity fields over the Aletsch

glacier at L-band. Top: Along-track displacement measured by means of

estimating the mutual shift between images. Middle: LOS displacement

measured by means of DInSAR. Bottom: Combination of both

measurements, where the colors indicate the magnitude and the

arrows indicate the direction of the displacement. Scene dimensions:

7.6 � 1.9 km2 (azimuth � slant range).

Fig. 20. Estimated 3-D surface velocity field at L-band, derived from

the combination of two orthogonal passes over the Aletsch glacier.




(ECV) by the Intergovernmental Panel on Climate Change(IPCC) [85]. In principle, SAR is known to have the

potential to provide such high-resolution soil moisture

maps at short time intervals. However, systematic moni-

toring of soil moisture with a high spatial and temporal

resolution is still a big challenge in Earth observation. The

advantage of VHR soil moisture mapping with airborne

fully polarimetric SAR is that information about the spatial

moisture distribution is obtained to enable precision farm-ing with a point-exact irrigation on the intrafield scale as

well as to improve the initialization of watershed runoff

models and with it also the model prediction of soil mois-

ture. High-resolution soil moisture maps from airborne

sensors are also used complementary to lower spatial scale

imaging, like from airborne radiometers or as it is opera-

tionally performed from the spaceborne Soil Moisture and

Ocean Salinity Mission (SMOS) [86].First soil moisture analyzes were carried out on single-

polarization intensities over bare soils, which led to the ill-

posed problem having both the soil moisture and the soil

roughness influence in just one acquired signal [87], [88].

With the emerging establishment of airborne fully polari-

metric SAR sensors, the observation space was enlarged by

SAR polarimetry to separate the soil roughness from the

soil moisture contribution on the signal for an unambig-uous retrieval of soil moisture over bare soils [89].

Moreover, the performance of conventional soil

moisture inversion algorithms is strongly compromised

by the presence of vegetation. Therefore, the fact that

agriculture fields are most of the year covered by vege-

tation makes the development of a distinct approach

essential. A promising solution is the use of fully pola-

rimetric SAR at lower frequencies providing an appropri-ate penetration as well as an observation space that allows

the interpretation and decomposition of different scatter-

ing contributions. Indeed, polarimetric decomposition

techniques have been successfully developed and used to

filter the disturbing vegetation contribution, allowing the

estimation of moisture content on the isolated surface

components [62], [90]. Fig. 21 shows the high-resolution

soil moisture maps for different moisture levels, whichwere obtained from airborne fully polarimetric L-band

data acquired by the E-SAR sensor at three different dates.

This analysis was conducted within the framework of the

agricultural bio/geophysical retrieval from frequent repeat-

pass SAR and optical imaging (AGRISAR) experiment in

2006. At the time of the first acquisition, the crop layer was

still short and sparse. Crop height and density increased

with time to the next acquisitions performed almost oneand two months later. However, the comparison with the

ground measurements indicated that despite the growth of

the vegetation cover, soil moisture was estimated with an

impressive root mean square error (RMSE) between 4 vol%

and 11 vol%. (The detailed validation is provided in [91],

while only the measured moisture trend is shown on the

bottom of Fig. 21.)

E. Forest Structure/3-D ImagingThe capability of the radar signal to penetrate volume-

tric structures (e.g., vegetation) leads to the possibility of

retrieving information about their vertical distribution.

This section is dedicated to describing two of the most

promising techniques having such goal, namely polarimet-ric SAR interferometry (Pol-InSAR) [92]–[94] and SAR

tomography (TomoSAR) [95], [96].

The introduction of Pol-InSAR techniques in the last

decade led to a breakthrough in quantitative estimation of

key forest structure parameters, such as forest height. In

fact, this is one of the most important parameters in fo-

restry and describes, along with basal area and tree species

or species composition, dynamic forest development, mod-eling, and inventory. Forest height is rather hard to be

measured on the ground; typical estimation errors are

given with 5%–10% accuracy, depending on the forest and

tree conditions, and increase with forest height and den-

sity. Height estimation from polarimetric single-baseline

and multibaseline data has been developed and demon-

strated through a series of airborne experiments, covering

Fig. 21. Top: Soil moisture maps obtained after applying polarimetric

decomposition techniques on L-band polarimetric data acquired at

three different dates. Bottom: Ground soil moisture measurements

versus mean vegetation height performed on the field indicated

by the red circle [91]. Dark blue: Soil moisture at 25-cm depth;

light blue: at 5-cm depth.




a large variety of forest conditions and types (temperate,

boreal, and tropical sites characterized by different stand,

terrain, and environmental conditions). The validation

against Lidar and/or ground measurements indicates

estimation accuracyVwidely independent of terrain and/

or forest conditionsVin the order of 10% compared to the

forest top height.An example of state-of-the-art Pol-InSAR forest height

products is shown in Fig. 22. On the left, an L-band SAR

image of the Traunstein forest site, acquired by the E-SAR

and located in southern Germany, is shown. The site is

characterized by a large variety of forest stand conditions

in the presence of locally variable topography. In the

center of Fig. 22, the forest height map derived from Pol-

InSAR data acquired at L-band is shown; on the right, thecorresponding validation plot against Lidar measurements

is presented.

Forest height maps with a spatial resolution better than

5 m are required for forest inventory applications in coun-

tries with a developed inventory system, as is the case for

central and northern Europe, implying sensor resolution

on the order of 1 m and better. A second, and even more

important application, that requires forest height mapswith high spatial resolution is the detection and quan-

tification of forest disturbance. The ability to detect

forest degradation at a high spatial resolution as caused,

for example, by selective logging, is critical for the

protection of forest environments. Fig. 23 shows a forest

height map of the Mawas region in Indonesia, derived

from P-band Pol-InSAR data acquired by E-SAR during

the Indrex-II campaign in 2004 [45]. In the forested part,tree height ranges between 15 and 27 m. The logging trails

caused by logging activities 10–15 years ago are clearly

visible as well as the higher degree of disturbances close to

the trails.

A direct possibility for resolving the vertical structure

of forests and other targets is the formation of a second

synthetic aperture in elevation, perpendicular to the flight

track. This technique is known as SAR tomography [95],

[96] and can be implemented through several parallel

tracks with horizontal and/or vertical displacements

(Fig. 24). A larger spatial extent of this aperture provides

a better vertical resolution (height), while a closer spacing

between adjacent tracks enlarges the unambiguous height

range (Nyquist criterion). As a result, ideal samplingconditions require a large number of regular passes.

However, for the airborne case, the number of images is

limited by the platform’s capacity for continuous acquisi-

tion and typically lies between 5 and 20.

In practice, after focusing and coregistering a set of

SAR images, one can apply beamforming techniques as a

function of height so as to retrieve vertical profiles [96].

An example of a linear Fourier reconstruction is depictedin Fig. 25, which represents a tomogram (azimuth–height

slice) of a forest.

Alternatively, other approaches can be chosen to over-

come the aforementioned sampling requirements. For

Fig. 22. L-band HV intensity image of the Traunstein test site (left),

forest height map computed from Pol-InSAR data and validation plot.

Fig. 23. Forest height map of the Mawas region, Indonesia, derived

from Pol-InSAR data acquired during the Indrex-II campaign in 2004.

Fig. 24. Tomographic acquisition geometry.




instance, super-resolution spectral estimators such as Capon,

and subspace methods have been employed [97]–[100].

Fig. 26 depicts the Capon tomogram of a target hidden

beneath vegetation [98].

Another important result consists of the first controlled

tomographic demonstration, where two corner reflectors

have been placed in a layover geometry [Fig. 27(a)]. Bymeans of Fourier beamforming, SAR tomography allowed

one to tell both scattering centers apart and to retrieve

their vertical distance of 3.5 m as well as their polarimetric

signature [Fig. 27(b)].

Last, recent work has concentrated on a robust reduc-

tion of the number of acquisitions for both regular [101]

and irregular track distributions [98], [102], [103]. In this

context, sparsity-driven techniques such as compressedsensing become possible [104], [105]. Fig. 28 presents the

two corner reflectors already introduced by Fig. 27(a) re-

lying on only five acquisitions with a distributed com-

pressed sensing approach [106].

F. Traffic MonitoringIn conventionally processed SAR images, moving vehi-

cles generally appear blurred and displaced from their ac-

tual positions [107]. The reason are the different Doppler

frequencies with respect to the stationary nonmoving

objects. A SAR traffic processor has the objective to detect

the moving vehicles and to determine their actual geog-

raphic positions, their velocities, and moving directions.

For this task, principally ground moving target indication(GMTI) algorithms originated in the military field can be

used [108]–[111].

Slowly moving vehicles are embedded in the clutter

Doppler spectrum caused by the radar platform motion.

For enabling the detection, at least a second receiving an-

tenna displaced in azimuth direction is required. Clutter

suppression can then be performed by using the displaced

phase center antenna (DPCA) technique [112]. Each an-tenna observes the scene from the same point in space at

Fig. 25. Polarimetric tomogram of a forest (Pauli basis) obtained with

21 acquisitions. The different colors indicate different scattering

contributions: Double-bounce reflections due to the ground-trunk

interaction (red), single-bounce reflections can be identified right next

to the azimuth coordinate of 350 m due to the absence of vegetation

(blue), and canopy contributions (green).

Fig. 26. SAR tomogram of two trucks obtained with 21 acquisitions.

One truck is located outside and the other inside the forest. The

absence of the ground-trunk double-bounce reflections for azimuth

coordinates next to the truck is due to the fact that the truck has been

placed on a path parallel to the flight direction (as shown in the bottom

right photograph).

Fig. 27. (a) Photo of two corner reflectors placed in a layover

geometry. (b) Tomographic intensity image representing their location

in the three Pauli channels carried out with the Fourier beamformer

and 21 acquisitions. The dominant odd-bounce reflection, typical of

trihedral corner reflectors, is retrieved (blue). The correct height

difference of 3.5 m is measured. Red: Even bounce; blue: Odd bounce;

green: Cross polarized.

Fig. 28. Tomogram of the two corner reflectors in layover obtained

with only five acquisitions and with a distributed compressed

sensing approach.




slightly different times. During this short time lag, thesignals from stationary targets remain the same, whereas

the signals from vehicles moving in range direction cause a

phase shift. By subtracting the signals received by the fore

and aft antenna, the clutter is suppressed but the moving

target signals remain in the data. If both clutter suppres-

sion and accurate parameter estimation are performed, at

least a three-channel system will be required [113].

In the multichannel case, the application of space–timeadaptive processing (STAP) techniques is more powerful

than DPCA [110], [111], [114]–[117]. By incorporating the

a priori known road network into the detection stage of the

GMTI algorithm and by ignoring vehicles moving on open

land (they may be of interest for military applications but

not for civilian road traffic monitoring), the system

complexity and the computational load can be reduced

significantly [118]–[120]. Furthermore, for many trafficmonitoring applications, a snapshot of the actual traffic

situation is sufficient so that complex and computation

time-consuming target tracking (which often can be found

in combination with STAP algorithms) is not required.

In [121], a fast traffic monitoring algorithm based on

a priori knowledge has been presented. The algorithm ope-

rates on range-compressed data so that no time-consuming

SAR imaging is necessary. The roads obtained from the

OpenStreetMap database are directly mapped to corre-

sponding azimuth beam center coordinates in the range-compressed data array. Only the intersections of the

moving vehicle signals with the roads are evaluated. A

traffic monitoring result obtained from that traffic proces-

sor applied on X-band data acquired with F-SAR is shown in

Fig. 29.

Principally for GMTI and traffic monitoring no high

resolution is required: lower data rates allow for faster

computation and a position accuracy (limited by the rangeand azimuth resolution) of a few meters often is sufficient.

However, high resolution in combination with GMTI al-

lows for the application of inverse SAR (ISAR) imaging

techniques [122]. Thus, not only moving vehicles can be

detected and their positions, velocities, and moving direc-

tions can be estimated, but they also can be refocused with

VHR. A further step would then be the application of pat-

tern recognition techniques, so that, for example, a discri-mination between motorbikes, passenger cars, and trucks

becomes possible. In Fig. 30, a high-resolution SAR image

of a junction is shown. The nonmoving passenger cars

waiting in front of the traffic lights clearly can be recog-

nized and their dimensions can be estimated. By sophis-

ticated ISAR techniques such sharp images can be

generated principally also from moving vehicles [123].

Fig. 29. Output of the traffic processor as Google Earth overlay. The

image shows a part of the highway A96 near Ammersee, Bavaria. The

color-coded symbols (color is velocity dependent) mark the estimated

‘‘true’’ geographical positions of the automatically detected road

vehicles. Dual-channel F-SAR data were used as input.

Fig. 30. High-resolution (0.25 � 0.25 m2) polarimetric X-band image

acquired with DLR’s F-SAR sensor.




V. SUMMARY AND OUTLOOK

High-resolution airborne SAR systems are revolutionizing

the information extraction in a wide range of remote

sensing applications. Examples are environmental remote

sensing, road traffic, hazard and disaster monitoring, as

well as reconnaissance and security related applications.

However, airborne sensors will remain limited to local

and/or regional aspects. For global coverage, spaceborne

SAR systems become mandatory. In a changing and dyna-

mic world, high-resolution and timely geospatial informa-

tion with global access and coverage becomes increasingly

important. SAR remote sensing will play a major role in

this task, since SAR is the only sensor technology that

combines all-weather, day-and-night with high-resolution

imaging capability.

One challenge for future spaceborne SAR systems is to

optimize the performance-to-cost ratio as much as possible

so that large-scale imaging with fast repeat intervals be-

comes possible. An attractive solution could be the usage

of a constellation of satellites in the form of small receiver

satellites acquiring the backscattered signal of active med-

ium Earth orbit (MEO) or geosynchronous Earth orbit

(GEO) satellites. This requires innovative concepts with

bistatic and multistatic system configurations. Digitalbeamforming for transmit and/or receive will solve thecontradiction posed by the antenna size in traditional SARsystems that prohibits the SAR sensor from having highazimuth resolution and a large swath width at the sametime. Digital beamforming is a clear trend for future sys-tems, allowing enormous flexibility in the sensor imagingmode, sensor calibration, interference removal, and ambi-guity suppression. These concepts will allow the imple-mentation of a flexible SAR sensor network with a fasteraccess time and almost continuous imaging capability,which is necessary for time-critical applications. High-flying platforms and unmanned vehicles will certainly actas a complementary platform for this network of sensors.Furthermore, radar satellites flying in close formation willallow the construction of sparse arrays with enhancedimaging capabilities.

Another important aspect for present and future mi-crowave sensors is the ability to provide quantitative andreliable data products to the user community. Today, thesensor information becomes multidimensional as differentsensor sources, polarizations, temporal and spatial base-lines, aspect angles, and frequencies are used for param-eter retrieval. h

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ABOUT T HE AUTHO RS

Andreas Reigber (Senior Member, IEEE) was born

in Munich, Germany, in 1970. He received the

diploma degree in physics from the University of

Constance, Germany, in 1997, the Ph.D. degree

from the University of Stuttgart, Stuttgart,

Germany, in 2001, and the habilitation from the

Berlin University of Technology, Berlin, Germany,

in 2008.

From 1996 to 2000, he was with the Microwave

and Radar Institute of the German Aerospace

Center (DLR), Wessling, Germany, working in the field of polarimetric SAR

tomography. In 2001, he joined the Antenna, Radar and Telecom

laboratories of the University of Rennes 1, Rennes, France, for a postdoc

on radar polarimetry and polarimetric interferometry. From 2002 to

2007, he was a Research Associate at the Computer Vision and Remote

Sensing laboratories of the Berlin University of Technology. Since 2008,

he has been back at the DLR Microwave and Radar Institute, where he is

now the Head of the SAR technology department and directing the

airborne SAR activities of the institute. His current main research

interests are the various aspects of multimodal SAR, like SAR interfer-

ometry, SAR polarimetry, SAR tomography, and time-frequency analyses,

as well as filtering and classification aspects of high-resolution SAR data.

Dr. Reigber received the EUSAR 2000 Student Prize Paper Award for

an article on SAR remote sensing of forests, the IEEE Transactions on

Geoscience and Remote Sensing Prize Paper Award in 2001 for a work on

polarimetric SAR tomography as well as the IEEE Geoscience and Remote

Sensing Letters Prize Paper Award in 2006 for a work on multipass SAR

processing.

Rolf Scheiber received the Diploma degree in

electrical engineering from the Technical University

of Munich, Munich, Germany in 1994 and the Ph.D.

degree in electrical engineering from the University

of Karlsruhe, Karlsruhe, Germany, in 2003, with a

dissertation on airborne SAR interferometry.

Since 1994, he has been with the Microwaves

and Radar Institute, German Aerospace Center

(DLR), Wessling, Germany, where he developed

the operational high-precision interferometric

SAR processor for its E-SAR airborne sensor. Since 2001, he has been

heading the SAR Signal Processing Group within the SAR Technology

Department. His research interests include algorithm development for

high-resolution airborne and spaceborne SAR focusing, SAR interferom-

etry, differential SAR interferometry, SAR tomography as well as radio

sounding algorithms and applications.

Dr. Scheiber was awarded, as co-author, the 1996 IEEE Transactions

on Geoscience and Remote Sensing Prize Paper Award for the

contribution BExtended chirp scaling algorithm for air- and spaceborne

SAR data processing in Stripmap and ScanSAR imaging modes.[

Marc Jager was born in Karlsruhe, Germany, in

1979. He received the B.A. (honors) degree in

computer science from the University of Cambridge,

Cambridge, U.K., in 2001 and the M.Sc. degree (with

distinction) in digital signal and image processing

fromDeMontfort University, Leicester, U.K., in 2003.

Currently, he works at the Germal Aerospace

Center (DLR), primarily on the polarimetric and

interferometric processing of airborne SAR data.

His research toward the Ph.D. degree concerns the

automatic extraction of semantic information from polarimetric and

polarimetric interferometric SAR data. His research interests include the

unsupervised classification and hierarchical segmentation of SAR data,

anisotropic diffusion, and graphical models for inference in object

recognition problems.

Mr. Jager received a Student Paper Award at the Sixth European

Conference on Synthetic Aperture Radar (EUSAR 2006).

Pau Prats-Iraola (Member, IEEE) was born in

Madrid, Spain, in 1977. He received the Ingeniero

degree in telecommunication engineering and the

Ph.D. degree from the Universitat Politecnica de

Catalunya (UPC), Barcelona, Spain, in 2001 and

2006, respectively.

In 2001, he was a Research Assistant at the

Institute of Geomatics, Spain. In 2002, he was at

the Department of Signal Theory and Communica-

tions, UPC, where he worked in the field of

airborne repeat-pass interferometry and airborne differential SAR

interferometry. From December 2002 to August 2006, he was an

Assistant Professor at the Department of Telecommunications and

Systems Engineering, Universitat AutŒnoma de Barcelona, Barcelona,

Spain. In 2006, he joined the Microwaves and Radar Institute, German

Aerospace Center (DLR), Wessling, Germany, where, since 2009, he has

been the Head of the Multimodal Algorithms Group. His research

interests include airborne and spaceborne high-resolution SAR proces-

sing, SAR interferometry, differential SAR interferometry, and motion

compensation for airborne systems.

Dr. Prats-Iraola was the recipient of the first prize of the Student

Paper Competition of the 2005 IEEE International Geoscience and

Remote Sensing Symposium, Seoul, Korea.




Irena Hajnsek (Senior Member, IEEE) received

the Dipl. degree (honors) from the Free University

of Berlin, Berlin, Germany, in 1996 and the Dr.

degree (honors) from the Friedrich Schiller Uni-

versity of Jena, Jena, Germany, in 2001.

Since November 2009, she has been a Profes-

sor of Earth Observation at the ETH Zurich Institute

of Environmental Engineering, Swiss Federal

Institute of Technology, Zurich, Switzerland and

at the same time the Head of the Polarimetric

SAR Interferometry research group at the German Aerospace Center

Microwaves and Radar Institute, Wessling, Germany. Her main research

interests are in electromagnetic propagation and scattering theory, radar

polarimetry, SAR and interferometric SAR data processing techniques, and

environmental parameter modeling and estimation. From 1996 to 1999, she

was with the Microwaves and Radar Institute (DLR-HF), German Aerospace

Center (DLR), Oberpfaffenhofen, Germany. From 1999 to 2000, shewaswith

the Institut d’Electronique et de Telecommunications de Rennes, University

of Rennes 1, France (for ten months) and with the Applied Electromagnetics

(AEL), St. Andrews, Scotland (for four months), in the frame of the EC-TMR

Radar Polarimetry Network. In 2005, she was a guest scientists at the

University of Adelaide, South Australia (for six weeks). She is the science

coordinator of the German satellite mission TanDEM-X.

Thomas Jagdhuber (Student Member, IEEE) was

born in Munich, Germany, in 1979. He received the

Diploma degree (with honors) in physical geogra-

phy, physics, and remote sensing from Ludwig

Maximilian University of Munich, Munich, Germany,

in 2006 and the Ph.D. degree from the University of

Potsdam, Potsdam, Germany, in 2012.

Currently, he is a Postdoctoral Fellow at the

Microwaves and Radar Institute, German Aero-

space Center, Wessling, Germany. His main re-

search interests include microwave remote sensing with an emphasis on

polarimetric and polarimetric interferometric techniques for hydrolog-

ical, agricultural, and snow parameter modeling and estimation. There-

fore, he is also involved in the analysis of several high-resolution F-SAR

campaigns for geophysical parameter estimation.

Dr. Jagdhuber serves as a reviewer for several international journals.

Konstantinos P.Papathanassiou (Senior Member,

IEEE) received the Dipl. Ing degree (honors) and

the Dr. degree (honors) from the Technical

University of Graz, Graz, Austria, in 1994 and

1999, respectively.

From 1992 to 1994, he was with the Institute for

Digital Image Processing (DIBAG) of Joanneum

Research, Graz, Austria. Between 1995 and 1999,

he worked at the Microwaves and Radar Institute

(HR) of the German Aerospace Center (DLR), Wes-

sling, Germany. From 1999 to 2000, he was a European Union (EU)

Postdoctoral Fellow with Applied Electromagnetics (AEL), St. Andrews,

Scotland. Since October 2000, he has been again with the Microwaves and

Radar Institute (HR) of the GermanAerospace Center (DLR). Currently, he is a

Senior Scientist leading the Information Retrieval research group at DLR-HR.

His main research interests are in polarimetric and interferometric

processing and calibration techniques, polarimetric SAR interferometry,

and the quantitative parameter estimation from SAR data, as well as in SAR

mission design and SAR mission performance analysis. He has more than

100 publications in international journals, conferences, and workshops.

Dr. Papathanassiou was awarded the IEEE Geoscience and Remote

Sensing Society (GRSS) International Geoscience and Remote Sensing

Symposium (IGARSS) Prize Paper Award in 1998, the Best Paper Award of

the European SAR Conference (EUSAR) in 2002, and the DLR science

award in 2002. In 2011, he was awarded with DLR’s Senior Scientist

Award.

Matteo Nannini received the Laurea degree in

telecommunication engineering from the Univer-

sity of Florence, Florence, Italy, in 2003, with a

thesis done in collaboration with the Microwaves

and Radar Institute (HR) of the German Aerospace

Center (DLR), Wessling, Germany and the Ph.D.

degree from the University of Karlsruhe, Karlsruhe,

Germany, in 2009, with a thesis on synthetic

aperture radar tomography.


and Radar Institute of DLR. His main research activities are synthetic

aperture radar processing, differential interferometry, tomography, and

radar sounding.

Dr. Nannini was awarded, as co-author, the best paper prize at the

joint SEAS & EMRS DTC Technical Conference in Edinburgh, U.K., in 2009

and 2010, respectively.

Esteban Aguilera was born inMendoza, Argentina,

on September 11, 1983. He received the Ingeniero

degree in information systems engineering from

the National Technological University, Mendoza,

Argentina, in 2007. Currently, he is working toward

the Ph.D. degree at the Microwaves and Radar

Institute, German Aerospace Center (DLR), Wessling,

Germany.

From 2008 to 2009, he worked as a Consultant

to a wide range of information technology (IT)

projects in Belgium and The Netherlands. His main research interests are

in polarimetric synthetic aperture radar (SAR), SAR interferometry, and

SAR tomography.

Stefan Baumgartner (Student Member, IEEE)

received the Dipl.-Ing. degree in electrical engineer-

ing and communication technology from the Graz

University of Technology, Graz, Austria, in 2004.


and Radar Institute (HR), German Aerospace

Center (DLR), Wessling, Germany. He is currently

with the Radar Concepts Department, where his

field of activity is the development of ground

moving target indication and parameter estima-

tion algorithms for future road-traffic-monitoring applications using

multichannel airborne and spaceborne synthetic aperture radars (SARs).

His research interests include SAR along-track interferometry, space-

time adaptive processing (STAP), time-frequency analysis, and other

advanced signal and imaging processing techniques.

Ralf Horn received the Dipl.-Ing. degree in

electrical engineering, with major subjects in

telecommunications and radio-frequency engi-

neering, from Ruhr Universitaet Bochum, Bochum,

Germany, in 1983.

Since 1983, he has been with the SAR Technol-

ogy Department, Microwaves and Radar Institute,

German Aerospace Center (DLR), Wessling,

Germany, where from 1983 to 1986, he was first a

Radar Engineer for the development of DLR’s

airborne Experimental SAR system E-SAR and was later a SAR Systems

Engineer. Then, in 1994, he was promoted to Team Leader of the

BAirborne SAR[ group of the SAR Technology Department. His current

responsibilities are focused on the project management for the new DLR

airborne SAR system F-SAR and on the management, coordination, and

execution of the DLR airborne SAR missions in Europe and abroad.




Anton Nottensteiner received the Dipl.-Ing. de-

gree in electrical engineering, with focus on

microwave technology, from the Technische Uni-

versitat Munchen, Munich, Germany, in 1980.

He has held a leading position in the microwave

system development, especially for satellite com-

munications. Since 1997, he has been with the SAR

Technology Department, Microwaves and Radar

Institute, German Aerospace Center (DLR),

Wessling, Germany. In 1998, he was promoted to

Team Leader of the BRadar Electronic[ group of the SAR Technology

Department. He is responsible for aircraft radar hardware development.

Alberto Moreira (Fellow, IEEE) was born in Sao

Jose dos Campos, Brazil, in 1962. He received the

B.S.E.E. and M.S.E.E. degrees from the Aeronauti-

cal Technological Institute (ITA), Sao Jose dos

Campos, Brasil, in 1984 and 1986, respectively,

and the Eng. Dr. degree (with honors) from the

Technical University of Munich, Munich, Germany,

in 1993.

From 1996 to 2001, he was the Chief Scientist

and Engineer with the SAR Technology Depart-

ment, German Aerospace Center (DLR), Wessling, Germany. Under his

leadership, the DLR airborne SAR system has been upgraded to operate

in innovative imaging modes like polarimetric SAR interferometry and

SAR tomography. Since 2001, he has been the Director of the Microwaves

and Radar Institute at DLR. The Institute contributes to several scientific

programs and space projects for actual and future airborne and space-

borne SAR missions like TerraSAR-X, TanDEM-X, SAR-Lupe, Sentinel-1,

and Tandem-L. The mission TanDEM-X, led by his Institute, has success-

fully started the operational phase in December 2010. He is the initiator

and Principal Investigator for this mission. Since 2003, he has been a

Full Professor with the Karlsruhe Institute of Technology, Karlsruhe,

Germany, in the field of microwave remote sensing. He has more than

300 publications in international conferences and journals and is the

holder of 15 patents in the radar and antenna field. His professional

interests and research areas encompass radar end-to-end system de-

sign and analysis, innovative microwave techniques and system con-

cepts, signal processing, and remote sensing applications.

Prof. Moreira is a member of the IEEE Geoscience and Remote Sensing

Society (GRSS) Administrative Committee (1999–2001, 2004–2013, 2010

as President, 2011–2013 as Past-President), was the Founder and Chair of

the GRSS German Chapter (2003–2008) and Associate Editor for the IEEE

Geoscience and Remote Sensing Letters (2003–2007) and for the IEEE

Transactions on Geoscience and Remote Sensing (2005–2011). He and his

colleagues received the GRSS Transactions Prize Paper Awards in 1997,

2001, and 2007 and the IEEE W.R.G. Baker Award in 2012. He is also the

recipient of the DLR Science Award (1995), the IEEE Nathanson Award for

the Young Radar Engineer of the Year (1999), and the IEEE Kiyo Tomiyasu

Field Award (2007). He served as a member of the Board of Directors of

the Information Technology Society of the German Association for

Electrical, Electronic and Information Technologies (2003–2008) and as

Chair of the Scientific and Technical Council of DLR (2009–2011). He has

contributed to the successful series of the European SAR conferences

(EUSAR) since 1996 as a member of the Technical Program Committee,

Technical Chairman (2000), Awards Chairman (2002–2004), General

Chairman (2006), and Co-Chairman (2008), and has served as General Co-

Chair for the 2012 International Geoscience and Remote Sensing

Symposium, Munich, Germany.



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